I just finished the guided project ‘Exploring eBay Car Sales Data’.
I’d appreciate any feedback on how I could improve my code, but also on how I could improve my written analysis as I really struggle with the storytelling aspect of these projects.
Course page: https://app.dataquest.io/m/294/guided-project%3A-exploring-ebay-car-sales-data/9/next-steps
Exploring Ebay Car Sales Data-checkpoint.ipynb (107.7 KB)
Click here to view the jupyter notebook file in a new tab
Hello @nur.mogammad.salaam Good work you’ve done here, I like the way you’ve provided explanations throughout your work.
As you continue learning, remember to come back to this project and add some visualizations, It will make this project more interesting.
Your code and markdown cells makes it a very easily understandable project. Your codes are so neat. You have done a great job with this project. You made everything look so easy!
There is only one thing I could find which might need your attention. Please have a look at line 21, you might want to change the inline comment. You have mentioned 30 years for all 3 operations.
Anyway great work. Keep up the good work. Happy learning.
Thank you for sharing your guided project! I agree with others that your code and markdown cells are very neat and easy to understand. I especially enjoyed all the inline text along with the code – it made your work very easy to follow. I learned some ways I could better abbreviate/nest my code by reviewing your project so thank you for that.
My only recommendation is taking a closer look at the registration years 2016+. Although it is true that it is not possible to register a car in the future, there were a decent number of entries for years 2017 and 2018. It is possible that the sellers are confusing model year with registration year. It is not uncommon for a car to be released with a model year number in the future (ex. you can buy a 2021 car model today in 2020) both to distinguish and market the vehicle. Of course my preference to keep these cars in the data set is subjective, but I think it’s important to recognize this possibility and make an argument for why you wish to keep or get rid of the data.
Thanks again – excellent project! Happy learning.
Wow, thanks for the insight, I didn’t even explore the registration years 2016+
I, personally, don’t think that’s a feasible reason to keep >=2017 registration years especially as this is a second-hand market. Since you can’t confirm that is what happened, I would personally just get rid of the rows implicated.